Towards Training-Free Scene Text Editing

• URL: http://arxiv.org/abs/2603.24571v1
• Date: Wed, 25 Mar 2026 17:50:31 GMT
• Title: Towards Training-Free Scene Text Editing
• Abstract summary: Scene text editing seeks to modify textual content in natural images.<n>Existing methods often require task-specific training or paired data.<n>We propose TextFlow, a training-free scene text editing framework.
• Score: 14.724015832146463
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scene text editing seeks to modify textual content in natural images while maintaining visual realism and semantic consistency. Existing methods often require task-specific training or paired data, limiting their scalability and adaptability. In this paper, we propose TextFlow, a training-free scene text editing framework that integrates the strengths of Attention Boost (AttnBoost) and Flow Manifold Steering (FMS) to enable flexible, high-fidelity text manipulation without additional training. Specifically, FMS preserves the structural and style consistency by modeling the visual flow of characters and background regions, while AttnBoost enhances the rendering of textual content through attention-based guidance. By jointly leveraging these complementary modules, our approach performs end-to-end text editing through semantic alignment and spatial refinement in a plug-and-play manner. Extensive experiments demonstrate that our framework achieves visual quality and text accuracy comparable to or superior to those of training-based counterparts, generalizing well across diverse scenes and languages. This study advances scene text editing toward a more efficient, generalizable, and training-free paradigm. Code is available at https://github.com/lyb18758/TextFlow

Related papers

• TextGuider: Training-Free Guidance for Text Rendering via Attention Alignment [68.91073792449201]
  We propose TextGuider, a training-free method that encourages accurate and complete text appearance.<n>Specifically, we analyze attention patterns in Multi-Modal Diffusion Transformer(MM-DiT) models, particularly for text-related tokens intended to be rendered in the image.<n>Our method achieves state-of-the-art performance in test-time text rendering, with significant gains in recall and strong results in OCR accuracy and CLIP score.
  arXiv  Detail & Related papers  (2025-12-10T06:18:30Z)
• SceneTextStylizer: A Training-Free Scene Text Style Transfer Framework with Diffusion Model [4.785724079054428]
  SceneTextStylizer is a training-free diffusion-based framework for flexible and high-fidelity style transfer of text in scene images.<n>Our method enables prompt-guided style transformation specifically for text regions, while preserving both text readability and stylistic consistency.
  arXiv  Detail & Related papers  (2025-10-13T02:11:57Z)
• FLUX-Text: A Simple and Advanced Diffusion Transformer Baseline for Scene Text Editing [15.626092015533072]
  Scene text editing aims to modify or add texts on images while ensuring text fidelity and overall visual quality consistent with the background.<n>Recent methods are primarily built on UNet-based diffusion models, which have improved scene text editing results, but still struggle with complex glyph structures.<n>We present textbfFLUX-Text, a simple and advanced multilingual scene text editing DiT method.
  arXiv  Detail & Related papers  (2025-05-06T08:56:28Z)
• DanceText: A Training-Free Layered Framework for Controllable Multilingual Text Transformation in Images [28.48453375674059]
  DanceText is a training-free framework for multilingual text editing in images.<n>It supports complex geometric transformations and achieves seamless foreground-background integration.
  arXiv  Detail & Related papers  (2025-04-18T23:46:32Z)
• Beyond Flat Text: Dual Self-inherited Guidance for Visual Text Generation [17.552733309504486]
  In real-world images, slanted or curved texts, especially those on cans, banners, or badges, appear as frequently as flat texts due to artistic design or layout constraints.<n>We introduce a new training-free framework, STGen, which accurately generates visual texts in challenging scenarios.
  arXiv  Detail & Related papers  (2025-01-10T11:44:59Z)
• Dynamic Typography: Bringing Text to Life via Video Diffusion Prior [73.72522617586593]
  We present an automated text animation scheme, termed "Dynamic Typography" It deforms letters to convey semantic meaning and infuses them with vibrant movements based on user prompts. Our technique harnesses vector graphics representations and an end-to-end optimization-based framework.
  arXiv  Detail & Related papers  (2024-04-17T17:59:55Z)
• Contextualized Diffusion Models for Text-Guided Image and Video Generation [67.69171154637172]
  Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing. We propose a novel and general contextualized diffusion model (ContextDiff) by incorporating the cross-modal context encompassing interactions and alignments between text condition and visual sample. We generalize our model to both DDPMs and DDIMs with theoretical derivations, and demonstrate the effectiveness of our model in evaluations with two challenging tasks: text-to-image generation, and text-to-video editing.
  arXiv  Detail & Related papers  (2024-02-26T15:01:16Z)
• Text-Only Training for Visual Storytelling [107.19873669536523]
  We formulate visual storytelling as a visual-conditioned story generation problem. We propose a text-only training method that separates the learning of cross-modality alignment and story generation.
  arXiv  Detail & Related papers  (2023-08-17T09:32:17Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• Improving Diffusion Models for Scene Text Editing with Dual Encoders [44.12999932588205]
  Scene text editing is a challenging task that involves modifying or inserting specified texts in an image. Recent advances in diffusion models have shown promise in overcoming these limitations with text-conditional image editing. We propose DIFFSTE to improve pre-trained diffusion models with a dual encoder design.
  arXiv  Detail & Related papers  (2023-04-12T02:08:34Z)
• Language Matters: A Weakly Supervised Pre-training Approach for Scene Text Detection and Spotting [69.77701325270047]
  This paper presents a weakly supervised pre-training method that can acquire effective scene text representations. Our network consists of an image encoder and a character-aware text encoder that extract visual and textual features. Experiments show that our pre-trained model improves F-score by +2.5% and +4.8% while transferring its weights to other text detection and spotting networks.
  arXiv  Detail & Related papers  (2022-03-08T08:10:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.